The Long Ashton Research Station Weather Generator (LARS-WG) is a stochastic weather generator used for the simulation of weather data at a single site under both current and future climate conditions using General Circulation Models (GCM). It was calibrated using the baseline (1981-2010) and evaluated to determine its suitability in generating synthetic weather data for 2020 and 2055 according to the projections of HadCM3 and BCCR-BCM2 GCMs under SRB1 and SRA1B scenarios at Mount Makulu (Latitude: 15.550 °S, Longitude: 28.250 °E, Elevation: 1213 meter), Zambia. Three weather parameters—precipitation, minimum and maximum temperature were simulated using LARS-WG v5.5 for observed station and AgMERRA reanalysis data for Mount Makulu. Monthly means and variances of observed and generated daily precipitation, maximum temperature and minimum temperature were used to evaluate the suitability of LARS-WG. Other climatic conditions such as wet and dry spells, seasonal frost and heat spells distributions were also used to assess the performance of the model. The results showed that these variables were modeled with good accuracy and LARS-WG could be used with high confidence to reproduce the current and future climate scenarios. Mount Makulu did not experience any seasonal frost. The average temperatures for the baseline (Observed station data: 1981-2010 and AgMERRA reanalysis: 1981-2010) were 21.33 °C and 22.21 °C, respectively. Using the observed station data, the average temperature under SRB1 (2020), SRA1B (2020), SRB1 (2055), SRA1B (2055) would be 21.90 °C, 21.94 °C, 22.83 °C and 23.18 °C, respectively. Under the AgMERRA reanalysis, the average temperatures would be 22.75 °C (SRB1: 2020), 22.80 °C (SRA1B: 2020), 23.69 °C (SRB1: 2055) and 24.05 °C (SRA1B: 2055). The HadCM3 and BCM2 GCMs ensemble mean showed that the number of days with precipitation would increase while the mean precipitation amount in 2020s and 2050s under SRA1B would reduce by 6.19% to 6.65%. Precipitation would increase under SRB1 (Observed), SRA1B, and SRB1 (AgMERRA) from 0.31% to 5.2% in 2020s and 2055s, respectively.
Global Climate Models (GCMs) from Intergovernmental Panel on Climate Change (IPCC) Third and Fifth Coupled Model Inter-comparison Projects (CMIP3 and CMIP5) are tools used to simulate the current and future climate change (maximum and minimum temperature, precipitation, solar radiation, surface pressure, wind, relative, and specific humidity, geopotential height, etc.) of the earth under different climate change scenarios [
The methods used to convert the coarse spatial resolution of GCM outputs into high-spatial resolution of point data [
A stochastic weather generator is a computer algorithm that uses existing meteorological records to produce a long series of synthetic daily weather data of unlimited length for a location based on the statistical characteristics of observed weather data at that location [
Agricultural productivity is sensitive to direct and indirect effects from changes in temperature, GHG concentration, and precipitation and in soil moisture and the distribution and frequency of infestation by pests and diseases, respectively. Predicted climate change scenarios may affect crop yield, growth rates, photosynthesis and transpiration rates, soil moisture availability, through changes of water use and agricultural inputs such as herbicides, pesticides, insecticides and fertilizers [
Stochastic weather generators are conventionally developed in two steps [
The LARS-WG version 5.5 also improves simulation of extreme weather events, such as extreme daily precipitation, long dry spells and heat waves [
The study site was Mount Makulu Research Station in Chilanga (Latitude: 15.550˚S, Longitude: 28.250˚E, Elevation: 1213 meter). The Region receives between 800 to 1000 mm of annual rainfall. The climate at the site is described as a wet and dry tropical and sub-tropical and is modified by altitude [
Historical climate data for daily rainfall (precip), minimum (Tmin) and maximum (Tmax) air temperature was obtained from the Zambia Meteorological Department (ZMD) for the period 1981-2010 and the Agricultural Modern-Era Retrospective Analysis for Research and Applications (AgMERRA) Climate Forcing Dataset for Agricultural Modeling for the period 1981-2010 [
values from 1980-2010 in order to form a “baseline or current period” climato- logy [
The Hadley Centre Couple Model version 3 (HadCM3) and Bergen Climate Model Version 2 (BCCR-BCM2) models were used in the IPCC Third and Fourth Assessments and also contributed to the Fifth Assessment Reports [
The Bergen Climate Model Version 2 (BCCR-BCM2) is a fully-coupled atmosphere-ocean-sea-ice model that provides state-of-the-art computer simulations of the present and future climate scenarios [
The Long Ashton Research Station Weather Generator (LARS-WG) is a stochastic weather generator [
LARS-WG as a stochastic weather generator utilizes a semi empirical distribution (SED) which is specified as the cumulative probability distribution function (CPDF) [
The process of generating local-scale daily climate scenario data in LARS-WG is divided into two steps of analysis and generator and briefly described by [
Analysis (site analysis and model calibration): Observed daily and AgMERRA reanalysis weather data for the site were analyzed to compute site parameters and these were stored in two files: a wgx-file (site parameters file) and a stx-file (additional statistics), respectively.
Generator (generation of synthetic weather data or site scenarios): the site parameter files derived from observed daily weather data was used to generate synthetic daily time series which statistically resembles the observed weather. The synthetic data corresponding to a particular climate change scenario may also be generated by applying global climate model-derived changes in precipitation, temperature and solar radiation to the LARS-WG parameter file [
to compare the probability distributions, T-test to compare means and F-test to compare standard deviations. The statistical tests used in LARS-WG v5.5 are based on the assumption that the observed/AgMERRA and synthetic weather data are both random samples from existing distributions and they test the null hypothesis that the two distributions are the same. The LARS-WG was validated by comparing statistics computed from a synthetic weather series generated by the weather generator against those from observed time series weather data [
The annual means of precipitation and temperature were computed using ensembles under SRA1B and SRB1 scenarios. If the calculated mean annual temperature and precipitation amounts (mm year-1) were within the 95% confidence interval (CI95) for the synthetic data, it was concluded that the statistic were simulated accurately for Mt. Makulu.
Use of at least 20 - 35 years of daily observed weather data is recommended to determine robust statistical parameters [
The calibrated LARS-WG stochastic weather generator was used to generate 30 years of synthetic daily precipitation, minimum and maximum temperature for Mount Makulu for the time slice 2011-2030 [near future (2020)] and 2046- 2065 [medium future (2055)] based on the SRB1 and SRA1B from HadCM3 and BCR2 GCMs for the study site (see
According to [
Scenario | Key assumption | CO2 concentration | ||
---|---|---|---|---|
2011-2030 | 2046-2065 | 2081-2100 | ||
B1 (“low” GHG emission scenario”) | Population convergence throughout the world, change in economic structure (pollutant reduction and introduction to clean technology resources). | 410 | 492 | 538 |
A1B (“medium” GHG emission scenario) | Rapid economic growth, maximum population growth during half century and after that decreasing trend, rapid modern and effective technology growth. | 418 | 541 | 674 |
A2 (“high” GHG emission scenario) | Rapid world population growth, heterogeneous economics in direction of regional conditions throughout the world. | 414 | 545 | 754 |
Note: CO2 concentration for the baseline scenario, 1960-1990, is 334 ppm.
where P() denotes probability based on observed data {vobs}. For each climatic variable, two values, p0 and pn, are fixed as p0 = 0 and pn = 1, with corresponding values of v0 = min{vobs} and vn = max{vobs}. To approximate the extreme values of a climatic variable accurately, some pi are assigned close to 0 for extremely low values of the variable and close to 1 for extremely high values and the remaining values of pi are distributed evenly on the probability scale. Because the probability of very low daily precipitation (<1 mm) is typically relatively high and such low precipitation has very little effect on the output of a process-based impact model, two values of v1 = 0.5 mm and v2 = 1 mm to approximate precipitation within the interval (0,1) with the corresponding probabilities calculated as
In LARS-WG v5.5, the maximum and minimum temperatures for dry and wet days are approximated by SEDs calculated for each month, with auto-and-cross- correlations calculated monthly. SEDs for climatic variables are calculated on a monthly basis by LARS-WG while some of the variables follow an annual cycle [
The coefficient of determination of a linear regression model is the quotient of the variances of the generated (Gen) and observed (Obs) values. The coefficient of determination is computed according to the Equation (2) below:
where
Estimating the distribution of random variables is an essential concern to statistics and its related disciplines as stated by [
1)
or
2)
If X and Y are discrete random variables, then f(x, y) must satisfy the equations below:
1)
or
2)
The Joint PDF Estimator developed by [
The Calibration and validation was carried out using the “Site Analysis” and “Qtest” function in LARS-WG model using two data sets, Observed station and AgMERRA reanalysis data, respectively. Performance of the weather generator during the calibration and the validation was checked using Kolmogorov-Simirnov (K-S) test, T-test and the F-test. The performance was also checked by using coefficient of correlation (R) and coefficient of determinant (R2). Evaluating the suitability of LARS-WG performance in simulating precipitation for Mount Makulu is presented in
Season | Wet/dry | N | K-S | p-value | Assessment |
---|---|---|---|---|---|
DJF | wet | 12 | 0.049 | 1.0000 | Perfect fit |
DJF | dry | 12 | 0.045 | 1.0000 | Perfect fit |
MAM | wet | 12 | 0.077 | 1.0000 | Perfect fit |
MAM | dry | 12 | 0.075 | 1.0000 | Perfect fit |
JJA | wet | 12 | 0.000 | 1.0000 | Perfect fit |
JJA | dry | 12 | 0.131 | 0.9824 | Very good fit |
SON | wet | 12 | 0.079 | 1.0000 | Very good fit |
SON | dry | 12 | 0.098 | 0.9997 | Perfect fit |
Season | Wet/dry | N | K-S | p-value | Assessment |
---|---|---|---|---|---|
DJF | wet | 12 | 0.030 | 1.0000 | Perfect fit |
DJF | dry | 12 | 0.193 | 0.7751 | Perfect fit |
MAM | wet | 12 | 0.034 | 1.0000 | Perfect fit |
MAM | dry | 12 | 0.175 | 0.8366 | Perfect fit |
JJA | wet | 12 | 0.000 | 1.0000 | Perfect fit |
JJA | dry | 12 | 1.000 | 0.0000 | Very poor fit |
SON | wet | 12 | 0.070 | 1.0000 | Very good fit |
SON | dry | 12 | 0.135 | 0.9761 | Perfect fit |
Month | N | K-S | p-value | Assessment |
---|---|---|---|---|
J | 12 | 0.073 | 1.0000 | Perfect fit |
F | 12 | 0.068 | 1.0000 | Perfect fit |
M | 12 | 0.121 | 0.9929 | Perfect fit |
A | 12 | 0.099 | 0.9997 | Perfect fit |
M | 12 | 0.206 | 0.6609 | Good fit |
J | 12 | 0.261 | 0.3593 | Very poor fit |
J | 12 | 0.566 | 0.0006 | Very poor fit |
A | 12 | 0.348 | 0.0955 | Very poor fit |
S | 12 | 0.305 | 0.1932 | Very poor fit |
O | 12 | 0.092 | 1.0000 | Perfect fit |
N | 12 | 0.170 | 0.8611 | Perfect fit |
D | 12 | 0.068 | 1.0000 | Perfect fit |
Month | N | K-S | p-value | Assessment |
---|---|---|---|---|
J | 12 | 0.132 | 0.9809 | Perfect fit |
F | 12 | 0.052 | 1.0000 | Perfect fit |
M | 12 | 0.055 | 1.0000 | Perfect fit |
A | 12 | 0.098 | 0.9997 | Perfect fit |
M | 12 | 0.348 | 0.0955 | Very poor fit |
J | 12 | 0.000 | 1.0000 | Perfect fit |
J | ND | |||
A | ND | |||
S | 12 | 0.217 | 0.5954 | Good fit |
O | 12 | 0.523 | 0.3975 | Perfect fit |
N | 12 | 0.040 | 1.0000 | Perfect fit |
D | 12 | 0.055 | 1.0000 | Perfect fit |
ND: Not determined.
to lack of precipitation during the period. The simulation of both minimum and maximum temperature for both data sets was perfect as presented in Tables 6-9. According to [
The seasonal frost and heat spells distributions and the statistical values are presented in
Comparison between the monthly mean and standard deviation of precipitation and temperature for the two data sets used in the analysis are presented in
Month | N | K-S | p-value | Assessment |
---|---|---|---|---|
J | 12 | 0.053 | 1.0000 | Perfect fit |
F | 12 | 0.106 | 0.9989 | Perfect fit |
M | 12 | 0.106 | 0.9989 | Perfect fit |
A | 12 | 0.053 | 1.0000 | Perfect fit |
M | 12 | 0.106 | 0.9989 | Perfect fit |
J | 12 | 0.106 | 0.9989 | Perfect fit |
J | 12 | 0.053 | 1.0000 | Perfect fit |
A | 12 | 0.053 | 1.0000 | Perfect fit |
S | 12 | 0.106 | 0.9989 | Perfect fit |
O | 12 | 0.106 | 0.9989 | Perfect fit |
N | 12 | 0.053 | 1.0000 | Perfect fit |
D | 12 | 0.106 | 0.9989 | Perfect fit |
Month | N | K-S | p-value | Assessment |
---|---|---|---|---|
J | 12 | 0.053 | 1.0000 | Perfect fit |
F | 12 | 0.053 | 1.0000 | Perfect fit |
M | 12 | 0.053 | 1.0000 | Perfect fit |
A | 12 | 0.106 | 0.9989 | Perfect fit |
M | 12 | 0.158 | 0.9125 | Perfect fit |
J | 12 | 0.158 | 0.9125 | Perfect fit |
J | 12 | 0.106 | 0.9989 | Perfect fit |
A | 12 | 0.106 | 0.9989 | Perfect fit |
S | 12 | 0.106 | 0.9989 | Perfect fit |
O | 12 | 0.106 | 0.9989 | Perfect fit |
N | 12 | 0.053 | 1.0000 | Perfect fit |
D | 12 | 0.106 | 0.9989 | Perfect fit |
Month | N | K-S | p-value | Assessment |
---|---|---|---|---|
J | 12 | 0.053 | 1.0000 | Perfect fit |
F | 12 | 0.053 | 1.0000 | Perfect fit |
M | 12 | 0.105 | 0.9991 | Perfect fit |
A | 12 | 0.106 | 0.9989 | Perfect fit |
M | 12 | 0.106 | 0.9989 | Perfect fit |
J | 12 | 0.106 | 0.9989 | Perfect fit |
J | 12 | 0.053 | 1.0000 | Perfect fit |
A | 12 | 0.106 | 0.9989 | Perfect fit |
S | 12 | 0.106 | 0.9989 | Perfect fit |
O | 12 | 0.106 | 0.9125 | Perfect fit |
N | 12 | 0.106 | 0.9989 | Perfect fit |
D | 12 | 0.053 | 1.0000 | Perfect fit |
Month | N | K-S | p-value | Assessment |
---|---|---|---|---|
J | 12 | 0.053 | 1.0000 | Perfect fit |
F | 12 | 0.053 | 1.0000 | Perfect fit |
M | 12 | 0.106 | 0.9989 | Perfect fit |
A | 12 | 0.053 | 1.0000 | Perfect fit |
M | 12 | 0.106 | 0.9989 | Perfect fit |
J | 12 | 0.106 | 0.9991 | Perfect fit |
J | 12 | 0.106 | 1.0000 | Perfect fit |
A | 12 | 0.106 | 0.9125 | Perfect fit |
S | 12 | 0.106 | 0.9989 | Perfect fit |
O | 12 | 0.106 | 0.9989 | Perfect fit |
N | 12 | 0.106 | 09989 | Perfect fit |
D | 12 | 0.105 | 0.9991 | Perfect fit |
AgMERRA reanalysis data | ||||
---|---|---|---|---|
Months | Frost/heat spells | Degree of freedom | KS-value | p-value |
DJF | No frost spells | - | - | - |
DJF | heat | 12 | 0.455 | 0.0110 |
MAM | No frost spells | - | - | - |
MAM | heat | 12 | 0.359 | 0.0786 |
JJA | No frost spells | - | - | - |
JJA | heat | 12 | 0.287 | 0.2522 |
SON | No frost spells | - | - | - |
SON | heat | 12 | 0.101 | 0.9995 |
Observed station data | ||||
Months | Frost/heat spells | Degree of freedom | KS-value | p-value |
DJF | No frost spells | - | - | - |
DJF | heat | 12 | 0.185 | 0.7833 |
MAM | No frost spells | - | - | - |
MAM | heat | 12 | 0.550 | 0.0010 |
JJA | No frost spells | - | - | - |
JJA | heat | 12 | 0.374 | 0.0596 |
SON | No frost spells | - | - | - |
SON | heat | 12 | 0.135 | 0.9761 |
temperature accurately. Results from statistical tests indicate that there is no significant difference in monthly means of the simulated monthly precipitation compared to the observations. Researchers such as [
In terms of standard deviation, LARS-WG showed an excellent performance for precipitation for all the month except February (over-estimated the standard deviation) and November (under-estimated the standard deviation). [
The HadCM3 and BCCR-BCM2 GCMs and B1 and A1B scenarios in LARS-WG version 5.5 were used in this study to generate future climate scenarios to better deal with uncertainties. Results for the observed station (1981-2010) and AgMERRA reanalysis (1981-2010) data indicated that the baseline had total annual precipitation of 841.2 mm/year in 64.5 days and total precipitation of 748.1 mm/year in 81.5 days, respectively. The difference in number of days and precipitation amounts is due to missing data in the observed station data. Computed mean ensemble outputs for SRB1 and SRA1B indicates that in 2020 and 2055 the number of days with precipitation would increase by 0.5 - 1.5 and 4 - 4.5 days under the observed station and reanalysis data, respectively. The outputs from observed station data indicated that number of days with precipitation and the amount of precipitation per year would reduce relative to the baseline. The mean amounts of precipitation would increase by 1.67%, 0.31% under SRB1 (2020), SRB1 (2055), respectively. Under the AgMERRA reanalysis data, results showed an increase in the mean amount of precipitation by 5.28%, 3.28%, 4.9% and 1.78% under SRA1B (2020), SRB1 (2020), SRA1B (2055) and SRB1 (2055), respectively. In future Mount Makulu would experience longer annual rainfall days and this finding is not in agreement as reported by [
The mean temperature in 2020 and 2055 would be 21.94˚C and 23.18˚C under SRA1B (Observed station data) and 21.90˚C and 22.83˚C under SRB1 (Observed station data). The temperatures would increase by 0.28˚C - 0.75˚C (2020) and 1.25˚C - 1.71˚C (2050) under SRB1, respectively. The changes in temperature are 0.40˚C - 0.83˚C (SRA1B: 2020), 1.65˚C - 2.08˚C (SRB1: 2055), under scenarios generated using observed station data. The changes in temperature under scenarios generated using AgMERRA reanalysis data would be 0.41˚C - 0.86˚C (SRB1: 2020) and 1.37˚C - 1.81˚C (SRB1: 2055) while the changes in temperature under SRA1B would be 0.47˚C - 0.92˚C (SRA1B: 2020) and 1.73˚C - 2.17˚C (SRA1B: 2055), respectively. The simulated changes in temperature at Mt. Makulu are within the predicted value by IPCC under B1 (1.1˚C - 2.9˚C) and A1B (1.4˚C - 6.4˚C). The observed station data (baseline) (1981-2010) mean temperature is 21.33˚C while the mean temperature for future scenarios are 21.90˚C, 21.94˚C, 22.83˚C, and 23.15˚C under SRB1 (2020), SRA1B (2020), SRB1 (2055) and SRA1B (2055), respectively. On the other hand, the AgMERRA data (1981-2010) mean temperature is 22.21˚C while the mean temperature for future scenarios are 22.75˚C, 22.80˚C, 23.69˚C, and 24.05˚C under SRB1 (2020), SRA1B (2020), SRB1 (2055) and SRA1B (2055), respectively. The results indicate an increasing trend in the mean temperature for Mt. Makulu. The projected temperature changes under A1B and B1 for Mt. Makulu are within the threshold projected by IPCC [1.4˚C - 6.4˚C (A1B) and 1.1˚C - 2.9˚C (B1)] [
The ensemble of the HadCM3 and BCM2 GCMs indicated that climate signal for precipitation amount in 2020 and 2055 would increase under observed station data (SRB1) and under AgMERRA reanalysis data (SRB1 and SRA1B). According to the National Climate Assessment [
The CI95 of the future climate scenarios for precipitation and temperature were computed for the two data sets. The CI95 shows values at the upper and lower end. The CI95 and time series for precipitation and temperature are presented in
Baseline | ||||||
---|---|---|---|---|---|---|
Observed modelled | AgMERRA reanalysis modelled | |||||
mean | lower | upper | mean | lower | upper | |
1981-2010 | ||||||
Tmin | 14.72 | 14.68 | 14.76 | 15.47 | 15.44 | 15.51 |
Tmax | 27.94 | 27.88 | 27.99 | 28.94 | 28.88 | 29.01 |
Tmean | 21.33 | 21.28 | 21.38 | 22.21 | 22.16 | 22.26 |
Precip | 854.50 | 781.60 | 927.50 | 764.60 | 715.00 | 814.30 |
Observed scenario | ||||||
a1b ensemble | b1 ensemble | |||||
mean | lower | upper | mean | lower | upper | |
2011-2040 | ||||||
Tmin | 15.33 | 15.29 | 15.36 | 15.26 | 15.21 | 15.30 |
Tmax | 28.54 | 28.50 | 28.58 | 28.53 | 28.48 | 28.59 |
Tmean | 21.94 | 21.90 | 21.97 | 21.90 | 21.85 | 21.95 |
Precip | 797.70 | 741.00 | 854.40 | 777.40 | 712.30 | 842.50 |
2041-2070 | ||||||
Tmin | 16.57 | 16.54 | 16.61 | 16.21 | 16.17 | 16.26 |
Tmax | 29.79 | 29.75 | 29.75 | 29.44 | 29.39 | 29.50 |
Tmean | 23.18 | 23.15 | 23.18 | 22.83 | 22.78 | 22.88 |
Precip | 801.60 | 744.20 | 859.00 | 767.00 | 703.00 | 831.00 |
AgMERRA reanalysis scenario | ||||||
a1b ensemble | b1 ensemble | |||||
mean | lower | upper | mean | lower | upper | |
2011-2040 | ||||||
Tmin | 16.10 | 16.07 | 16.14 | 16.04 | 16.00 | 16.07 |
Tmax | 29.49 | 29.43 | 29.54 | 29.46 | 29.40 | 29.51 |
Tmean | 22.80 | 22.75 | 22.84 | 22.75 | 22.70 | 22.79 |
Precip | 805.00 | 754.90 | 855.20 | 789.70 | 741.10 | 838.20 |
2041-2070 | ||||||
Tmin | 17.36 | 17.32 | 17.39 | 17.00 | 16.97 | 17.04 |
Tmax | 30.73 | 30.67 | 30.78 | 30.37 | 30.31 | 30.42 |
Tmean | 24.05 | 24.00 | 24.09 | 23.69 | 23.64 | 23.73 |
Precip | 802.30 | 752.40 | 852.20 | 778.20 | 730.50 | 825.90 |
The Joint Probability Distribution Functions (JPDFs) were estimated for precipitation and temperature using the observed station and AgMERRA reanalysis data. The JPDFs and Joint Cumulative Distribution Functions (JCDFs) for precipitation and temperature for the observed and AgMERRA reanalysis data are presented in
Precip | Tmax | Tmin | |||||||
---|---|---|---|---|---|---|---|---|---|
jpdf | jcdf | Overall fitness | jpdf | jcdf | Overall fitness | jpdf | jcdf | Overall fitness | |
Observed | 1.86 | 0.58 | 0.92 | 3.55 | 0.00 | 0.81 | 0.00 | 0.00 | 0.96 |
AgMERRA | 0.00 | 0.54 | 0.76 | 1.57 | 0.64 | 0.80 | 1.20 | 0.80 | 0.77 |
maximum temperature were 0.76, 0.77 and 0.80, respectively. The LARS-WG semi-empirical distribution models precipitation and temperature as a step function and therefore its shape only approximately follows the shape of the observed/AgMERRA values. The shape of the distribution for observed and AgMERRA data approximately follows that of the synthetic values as presented in
Agricultural productivity is sensitive to direct changes in maximum and minimum temperature, precipitation, and GHG concentration. Indirect changes are soil moisture and the distribution and frequency of infestation by pests and diseases. Future climate change will affect crop yield, photosynthesis and transpiration rates, growth rates, and soil moisture availability, through changes of water use and agricultural inputs (herbicides, pesticides, insecticides, fertilizers) [
In this study three meteorological parameters from the observed station and AgMERRA reanalysis data for Mount Makulu site―precipitation, minimum and maximum temperature―were simulated using LARS-WG5.5 stochastic weather generator. The results showed that these parameters were modeled with good accuracy. The LARS-WG could be used to generate climate scenarios for the current and future scenarios for Mount Makulu. LARS-WG simulated the monthly mean precipitation, minimum and maximum temperatures which are accurate with the correlation between the observed/AgMERRA reanalysis and g generated monthly means being 0.99. Results showed that the maximum and minimum temperature for Mount Makulu would increase during 2020s and 2055s under SRB1 and SRA1B. The ensemble mean of the HadCM3 and BCM2 GCMs indicated that climate signal for precipitation amount in 2020 and 2055 would increase under observed station data and reduce under AgMERRA reanalysis data. Climate scenarios of more than one climate model are necessary for providing insights into climate model uncertainties as well as developing alternative adaptation and mitigation strategies.
The researchers wish to thank the Agricultural Productivity Programme for Southern Africa (APPSA) under the Zambia Agricultural Research Institute (ZARI) Central Station for financing the publication of this paper. The researchers also wish to thank Prof. Mikhail A. Semenov for the provision of Long Ashton Research Station Weather Generator and license used in this study. Thanks are also extended to Dr. Alexander C. Ruane from National Aeronautics and Space Administration (NASA) and Zambia Meteorological Department (ZMD) for the provision of daily weather data sets.
The authors declare no conflict of interest.
Chisanga, C.B., Phiri, E. and Chinene, V.R.N. (2017) Statistical Downscaling of Precipitation and Temperature Using Long Ashton Research Station Weather Generator in Zambia: A Case of Mount Makulu Agriculture Research Station. American Journal of Climate Change, 6, 487-512. https://doi.org/10.4236/ajcc.2017.63025